Overview

Dataset statistics

Number of variables15
Number of observations4703
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory652.6 KiB
Average record size in memory142.1 B

Variable types

Numeric12
Categorical3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
actor_1_fb_likes is highly overall correlated with other_actors_fb_likesHigh correlation
budget is highly overall correlated with gross and 1 other fieldsHigh correlation
country_UK is highly overall correlated with country_USAHigh correlation
country_USA is highly overall correlated with country_UKHigh correlation
critic_reviews_ratio is highly overall correlated with title_yearHigh correlation
gross is highly overall correlated with budget and 1 other fieldsHigh correlation
num_voted_users is highly overall correlated with budget and 1 other fieldsHigh correlation
other_actors_fb_likes is highly overall correlated with actor_1_fb_likesHigh correlation
title_year is highly overall correlated with critic_reviews_ratioHigh correlation
country_UK is highly imbalanced (56.6%)Imbalance
budget is highly skewed (γ1 = 49.02395721)Skewed
director_fb_likes has 825 (17.5%) zerosZeros
facenumber_in_poster has 2019 (42.9%) zerosZeros
movie_fb_likes has 2086 (44.4%) zerosZeros

Reproduction

Analysis started2024-04-11 09:51:16.816670
Analysis finished2024-04-11 09:51:50.102013
Duration33.29 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

duration
Real number (ℝ)

Distinct164
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.63066
Minimum14
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:50.251011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile84
Q194
median104
Q3118
95-th percentile146
Maximum330
Range316
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.562204
Coefficient of variation (CV)0.20769646
Kurtosis11.779179
Mean108.63066
Median Absolute Deviation (MAD)11
Skewness2.2280838
Sum510890
Variance509.05305
MonotonicityNot monotonic
2024-04-11T11:51:50.492008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 143
 
3.0%
100 134
 
2.8%
98 130
 
2.8%
101 130
 
2.8%
97 125
 
2.7%
93 120
 
2.6%
99 120
 
2.6%
94 120
 
2.6%
95 119
 
2.5%
106 108
 
2.3%
Other values (154) 3454
73.4%
ValueCountFrequency (%)
14 1
< 0.1%
20 1
< 0.1%
25 1
< 0.1%
34 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
45 2
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
330 1
< 0.1%
325 1
< 0.1%
300 1
< 0.1%
293 1
< 0.1%
289 1
< 0.1%
280 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
251 2
< 0.1%
240 2
< 0.1%

director_fb_likes
Real number (ℝ)

ZEROS 

Distinct429
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.17223
Minimum0
Maximum23000
Zeros825
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:50.708006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median52
Q3209
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)201

Descriptive statistics

Standard deviation2861.8195
Coefficient of variation (CV)4.0297542
Kurtosis26.029513
Mean710.17223
Median Absolute Deviation (MAD)52
Skewness5.1181934
Sum3339940
Variance8190010.9
MonotonicityNot monotonic
2024-04-11T11:51:50.923042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 825
 
17.5%
3 65
 
1.4%
6 61
 
1.3%
7 58
 
1.2%
11 56
 
1.2%
2 56
 
1.2%
4 54
 
1.1%
10 51
 
1.1%
12 48
 
1.0%
5 48
 
1.0%
Other values (419) 3381
71.9%
ValueCountFrequency (%)
0 825
17.5%
2 56
 
1.2%
3 65
 
1.4%
4 54
 
1.1%
5 48
 
1.0%
6 61
 
1.3%
7 58
 
1.2%
8 47
 
1.0%
9 46
 
1.0%
10 51
 
1.1%
ValueCountFrequency (%)
23000 1
 
< 0.1%
22000 8
 
0.2%
21000 10
 
0.2%
18000 4
 
0.1%
17000 20
0.4%
16000 28
0.6%
15000 2
 
< 0.1%
14000 30
0.6%
13000 26
0.6%
12000 17
0.4%

actor_1_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct843
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6817.3957
Minimum0
Maximum640000
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:51.127407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116.1
Q1637
median1000
Q311000
95-th percentile24000
Maximum640000
Range640000
Interquartile range (IQR)10363

Descriptive statistics

Standard deviation14982.445
Coefficient of variation (CV)2.1976786
Kurtosis720.98565
Mean6817.3957
Median Absolute Deviation (MAD)790
Skewness19.549467
Sum32062212
Variance2.2447365 × 108
MonotonicityNot monotonic
2024-04-11T11:51:51.351401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 417
 
8.9%
11000 207
 
4.4%
2000 187
 
4.0%
3000 148
 
3.1%
12000 133
 
2.8%
13000 126
 
2.7%
14000 121
 
2.6%
10000 109
 
2.3%
18000 108
 
2.3%
22000 79
 
1.7%
Other values (833) 3068
65.2%
ValueCountFrequency (%)
0 14
0.3%
2 6
0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 4
 
0.1%
6 3
 
0.1%
7 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 3
 
0.1%
ValueCountFrequency (%)
640000 1
 
< 0.1%
260000 2
 
< 0.1%
164000 2
 
< 0.1%
137000 2
 
< 0.1%
87000 8
 
0.2%
77000 1
 
< 0.1%
49000 27
0.6%
46000 1
 
< 0.1%
45000 5
 
0.1%
44000 2
 
< 0.1%

gross
Real number (ℝ)

HIGH CORRELATION 

Distinct4146
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45085643
Minimum162
Maximum7.6050585 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:51.626429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile100669.6
Q16494675
median24848292
Q354548936
95-th percentile1.7099911 × 108
Maximum7.6050585 × 108
Range7.6050568 × 108
Interquartile range (IQR)48054262

Descriptive statistics

Standard deviation64148103
Coefficient of variation (CV)1.4228055
Kurtosis16.705866
Mean45085643
Median Absolute Deviation (MAD)20807704
Skewness3.3289438
Sum2.1203778 × 1011
Variance4.1149791 × 1015
MonotonicityNot monotonic
2024-04-11T11:51:51.896428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24848292 458
 
9.7%
5000000 4
 
0.1%
3000000 3
 
0.1%
218051260 3
 
0.1%
177343675 3
 
0.1%
8000000 3
 
0.1%
13401683 2
 
< 0.1%
800000 2
 
< 0.1%
22494487 2
 
< 0.1%
21028755 2
 
< 0.1%
Other values (4136) 4221
89.8%
ValueCountFrequency (%)
162 1
< 0.1%
423 1
< 0.1%
607 1
< 0.1%
703 1
< 0.1%
721 1
< 0.1%
728 1
< 0.1%
828 1
< 0.1%
1029 1
< 0.1%
1100 1
< 0.1%
1111 1
< 0.1%
ValueCountFrequency (%)
760505847 1
< 0.1%
658672302 1
< 0.1%
652177271 1
< 0.1%
623279547 1
< 0.1%
533316061 1
< 0.1%
474544677 1
< 0.1%
460935665 1
< 0.1%
458991599 1
< 0.1%
448130642 1
< 0.1%
436471036 1
< 0.1%

num_voted_users
Real number (ℝ)

HIGH CORRELATION 

Distinct4593
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87783.318
Minimum5
Maximum1689764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:52.161803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1099.4
Q110774
median37952
Q3101938
95-th percentile343205.1
Maximum1689764
Range1689759
Interquartile range (IQR)91164

Descriptive statistics

Standard deviation140733.28
Coefficient of variation (CV)1.6031894
Kurtosis23.651174
Mean87783.318
Median Absolute Deviation (MAD)32809
Skewness3.9557772
Sum4.1284494 × 108
Variance1.9805856 × 1010
MonotonicityNot monotonic
2024-04-11T11:51:52.385073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3119 3
 
0.1%
2541 3
 
0.1%
3665 3
 
0.1%
9903 2
 
< 0.1%
6069 2
 
< 0.1%
80639 2
 
< 0.1%
25870 2
 
< 0.1%
1231 2
 
< 0.1%
3943 2
 
< 0.1%
53 2
 
< 0.1%
Other values (4583) 4680
99.5%
ValueCountFrequency (%)
5 2
< 0.1%
19 1
< 0.1%
28 1
< 0.1%
37 1
< 0.1%
40 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
50 1
< 0.1%
53 2
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
1689764 1
< 0.1%
1676169 1
< 0.1%
1468200 1
< 0.1%
1347461 1
< 0.1%
1324680 1
< 0.1%
1251222 1
< 0.1%
1238746 1
< 0.1%
1217752 1
< 0.1%
1215718 1
< 0.1%
1155770 1
< 0.1%

facenumber_in_poster
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3567935
Minimum0
Maximum43
Zeros2019
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:52.579072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0086637
Coefficient of variation (CV)1.4804491
Kurtosis55.770377
Mean1.3567935
Median Absolute Deviation (MAD)1
Skewness4.5646736
Sum6381
Variance4.03473
MonotonicityNot monotonic
2024-04-11T11:51:52.731107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
Other values (9) 30
 
0.6%
ValueCountFrequency (%)
0 2019
42.9%
1 1179
25.1%
2 665
 
14.1%
3 359
 
7.6%
4 190
 
4.0%
5 100
 
2.1%
6 67
 
1.4%
7 45
 
1.0%
8 34
 
0.7%
9 15
 
0.3%
ValueCountFrequency (%)
43 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
15 5
 
0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
0.1%
11 5
 
0.1%
10 10
0.2%
9 15
0.3%

budget
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct432
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39306827
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:52.937116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile800000
Q17500000
median20000000
Q340000000
95-th percentile1.25 × 108
Maximum1.22155 × 1010
Range1.22155 × 1010
Interquartile range (IQR)32500000

Descriptive statistics

Standard deviation2.02669 × 108
Coefficient of variation (CV)5.1560762
Kurtosis2820.5211
Mean39306827
Median Absolute Deviation (MAD)15000000
Skewness49.023957
Sum1.8486001 × 1011
Variance4.1074723 × 1016
MonotonicityNot monotonic
2024-04-11T11:51:53.184575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000 442
 
9.4%
30000000 145
 
3.1%
15000000 141
 
3.0%
25000000 139
 
3.0%
10000000 137
 
2.9%
40000000 131
 
2.8%
35000000 120
 
2.6%
50000000 104
 
2.2%
5000000 102
 
2.2%
60000000 94
 
2.0%
Other values (422) 3148
66.9%
ValueCountFrequency (%)
218 1
 
< 0.1%
1100 1
 
< 0.1%
4500 1
 
< 0.1%
7000 3
0.1%
9000 1
 
< 0.1%
10000 2
< 0.1%
14000 1
 
< 0.1%
15000 2
< 0.1%
20000 3
0.1%
22000 1
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
< 0.1%
4200000000 1
< 0.1%
2500000000 1
< 0.1%
2400000000 1
< 0.1%
2127519898 1
< 0.1%
1100000000 1
< 0.1%
1000000000 1
< 0.1%
700000000 2
< 0.1%
600000000 1
< 0.1%
553632000 1
< 0.1%

title_year
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.1112
Minimum1916
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:53.406445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1916
5-th percentile1978
Q11999
median2005
Q32010
95-th percentile2015
Maximum2016
Range100
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.50241
Coefficient of variation (CV)0.0062446132
Kurtosis7.3909201
Mean2002.1112
Median Absolute Deviation (MAD)6
Skewness-2.2877603
Sum9415929
Variance156.31026
MonotonicityNot monotonic
2024-04-11T11:51:53.722324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2009 252
 
5.4%
2006 235
 
5.0%
2008 222
 
4.7%
2010 221
 
4.7%
2011 215
 
4.6%
2005 215
 
4.6%
2014 214
 
4.6%
2013 213
 
4.5%
2004 206
 
4.4%
2012 203
 
4.3%
Other values (81) 2507
53.3%
ValueCountFrequency (%)
1916 1
< 0.1%
1920 1
< 0.1%
1925 1
< 0.1%
1927 1
< 0.1%
1929 2
< 0.1%
1930 1
< 0.1%
1932 1
< 0.1%
1933 2
< 0.1%
1934 1
< 0.1%
1935 1
< 0.1%
ValueCountFrequency (%)
2016 82
 
1.7%
2015 183
3.9%
2014 214
4.6%
2013 213
4.5%
2012 203
4.3%
2011 215
4.6%
2010 221
4.7%
2009 252
5.4%
2008 222
4.7%
2007 197
4.2%

aspect_ratio
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1255305
Minimum1.18
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:53.943389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.78
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.82
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.63838629
Coefficient of variation (CV)0.3003421
Kurtosis377.18399
Mean2.1255305
Median Absolute Deviation (MAD)0
Skewness17.406589
Sum9996.37
Variance0.40753706
MonotonicityNot monotonic
2024-04-11T11:51:54.114389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.35 2499
53.1%
1.85 1870
39.8%
1.37 97
 
2.1%
1.78 79
 
1.7%
1.66 63
 
1.3%
1.33 37
 
0.8%
2.2 14
 
0.3%
2.39 14
 
0.3%
16 8
 
0.2%
2 4
 
0.1%
Other values (10) 18
 
0.4%
ValueCountFrequency (%)
1.18 1
 
< 0.1%
1.2 1
 
< 0.1%
1.33 37
 
0.8%
1.37 97
2.1%
1.44 1
 
< 0.1%
1.5 2
 
< 0.1%
1.66 63
1.3%
1.75 3
 
0.1%
1.77 1
 
< 0.1%
1.78 79
1.7%
ValueCountFrequency (%)
16 8
 
0.2%
2.76 3
 
0.1%
2.55 2
 
< 0.1%
2.4 3
 
0.1%
2.39 14
 
0.3%
2.35 2499
53.1%
2.24 1
 
< 0.1%
2.2 14
 
0.3%
2 4
 
0.1%
1.85 1870
39.8%

movie_fb_likes
Real number (ℝ)

ZEROS 

Distinct836
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7779.7997
Minimum0
Maximum349000
Zeros2086
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:54.335826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median181
Q35000
95-th percentile41900
Maximum349000
Range349000
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation19611.482
Coefficient of variation (CV)2.520821
Kurtosis40.309513
Mean7779.7997
Median Absolute Deviation (MAD)181
Skewness4.9742692
Sum36588398
Variance3.8461023 × 108
MonotonicityNot monotonic
2024-04-11T11:51:54.574920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2086
44.4%
1000 103
 
2.2%
11000 80
 
1.7%
10000 79
 
1.7%
12000 59
 
1.3%
13000 58
 
1.2%
2000 54
 
1.1%
15000 51
 
1.1%
14000 46
 
1.0%
16000 46
 
1.0%
Other values (826) 2041
43.4%
ValueCountFrequency (%)
0 2086
44.4%
4 2
 
< 0.1%
5 1
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
17 3
 
0.1%
ValueCountFrequency (%)
349000 1
< 0.1%
199000 1
< 0.1%
197000 1
< 0.1%
191000 1
< 0.1%
190000 1
< 0.1%
175000 1
< 0.1%
165000 1
< 0.1%
164000 1
< 0.1%
153000 1
< 0.1%
150000 1
< 0.1%

country_UK
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size202.5 KiB
0
4283 
1
 
420

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4703
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4283
91.1%
1 420
 
8.9%

Length

2024-04-11T11:51:54.814921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T11:51:54.988034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4283
91.1%
1 420
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 4283
91.1%
1 420
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4703
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4283
91.1%
1 420
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4703
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4283
91.1%
1 420
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4283
91.1%
1 420
 
8.9%

country_USA
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size202.5 KiB
1
3575 
0
1128 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4703
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3575
76.0%
0 1128
 
24.0%

Length

2024-04-11T11:51:55.132034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T11:51:55.281034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3575
76.0%
0 1128
 
24.0%

Most occurring characters

ValueCountFrequency (%)
1 3575
76.0%
0 1128
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4703
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3575
76.0%
0 1128
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4703
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3575
76.0%
0 1128
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3575
76.0%
0 1128
 
24.0%

other_actors_fb_likes
Real number (ℝ)

HIGH CORRELATION 

Distinct2041
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2380.9303
Minimum0
Maximum137748
Zeros32
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:55.465029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1480.5
median1017
Q31581
95-th percentile13000
Maximum137748
Range137748
Interquartile range (IQR)1100.5

Descriptive statistics

Standard deviation5262.0495
Coefficient of variation (CV)2.2100813
Kurtosis107.20966
Mean2380.9303
Median Absolute Deviation (MAD)547
Skewness6.8977295
Sum11197515
Variance27689165
MonotonicityNot monotonic
2024-04-11T11:51:55.692029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
0.7%
2000 30
 
0.6%
3000 19
 
0.4%
12000 16
 
0.3%
15000 16
 
0.3%
13000 15
 
0.3%
4000 15
 
0.3%
14000 14
 
0.3%
22000 13
 
0.3%
24000 12
 
0.3%
Other values (2031) 4521
96.1%
ValueCountFrequency (%)
0 32
0.7%
2 10
 
0.2%
3 3
 
0.1%
4 5
 
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 4
 
0.1%
8 8
 
0.2%
9 6
 
0.1%
10 4
 
0.1%
ValueCountFrequency (%)
137748 1
 
< 0.1%
50000 1
 
< 0.1%
46000 1
 
< 0.1%
42000 1
 
< 0.1%
40000 2
 
< 0.1%
39000 1
 
< 0.1%
38000 1
 
< 0.1%
37000 2
 
< 0.1%
36000 8
0.2%
35000 1
 
< 0.1%

critic_reviews_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct3844
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8893224
Minimum0.037037037
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size202.5 KiB
2024-04-11T11:51:55.944025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.037037037
5-th percentile0.2016469
Q10.38334609
median0.62222222
Q31.0902912
95-th percentile2.2437546
Maximum25
Range24.962963
Interquartile range (IQR)0.7069451

Descriptive statistics

Standard deviation1.0070265
Coefficient of variation (CV)1.1323525
Kurtosis161.81569
Mean0.8893224
Median Absolute Deviation (MAD)0.29255189
Skewness9.1138071
Sum4182.4832
Variance1.0141023
MonotonicityNot monotonic
2024-04-11T11:51:56.174023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 43
 
0.9%
0.5 29
 
0.6%
0.3333333333 19
 
0.4%
2 19
 
0.4%
0.6666666667 16
 
0.3%
1.5 13
 
0.3%
0.8 12
 
0.3%
0.4 11
 
0.2%
0.5714285714 10
 
0.2%
3 8
 
0.2%
Other values (3834) 4523
96.2%
ValueCountFrequency (%)
0.03703703704 1
< 0.1%
0.04802123552 1
< 0.1%
0.05263157895 1
< 0.1%
0.05869565217 1
< 0.1%
0.0625 1
< 0.1%
0.06482504604 1
< 0.1%
0.07474352711 1
< 0.1%
0.07575757576 1
< 0.1%
0.07692307692 1
< 0.1%
0.07851239669 1
< 0.1%
ValueCountFrequency (%)
25 1
< 0.1%
21 2
< 0.1%
18 1
< 0.1%
9.428571429 1
< 0.1%
9 1
< 0.1%
8.75 1
< 0.1%
8.5 1
< 0.1%
8 2
< 0.1%
7 1
< 0.1%
6.759259259 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size202.5 KiB
2
3018 
1
1327 
3
 
204
0
 
154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4703
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Length

2024-04-11T11:51:56.372573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-11T11:51:56.557504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring characters

ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4703
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4703
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3018
64.2%
1 1327
28.2%
3 204
 
4.3%
0 154
 
3.3%

Interactions

2024-04-11T11:51:46.848801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:17.697576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:20.239213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:22.609134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:26.095595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:28.766979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:31.351952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:33.736875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:36.265779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:38.757793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:41.163193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:44.206710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:47.088151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:17.957571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:20.426214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:22.813130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:26.307633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:28.986973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:31.553949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:33.952393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:36.465776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:38.950792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:41.371237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:44.424223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:47.266151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:18.195572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:20.595211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:22.982164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:26.513632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:29.185978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:31.735464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:34.147773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:36.675297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:39.123785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:41.566606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:44.613223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:47.481149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:18.411569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:20.795727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:23.186161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:26.732628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:29.400971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:31.946986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:34.347772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:36.868294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:39.321787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:41.771811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:44.834608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:47.692149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:18.628877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:20.991805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:24.380625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:26.990672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:29.618970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:32.136983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:34.583279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:37.089291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:39.521784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:41.979810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:45.074607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:47.914144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:18.834876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:21.186805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:24.648663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:27.231594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:29.850968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:32.329981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:34.808284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:37.311805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:39.751782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:42.230805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:45.288604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:48.096669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:19.007872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:21.378800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:24.841547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:27.407591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:30.042967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:32.547981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:34.996305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:37.512806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:39.950299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:42.420804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:45.503599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:48.322548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:19.224696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:21.581800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:25.041589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:27.677622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:30.265966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:32.758978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:35.207274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:37.727802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:40.159209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:42.630316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:45.729599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:48.529548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:19.403223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:21.773096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:25.228604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:27.910621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:30.472964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:32.936975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:35.393272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:37.931797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:40.342205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:42.822221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:45.948599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:48.738544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:19.644222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:21.996094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:25.446602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:28.133617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:30.682959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:33.144492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:35.624271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:38.164799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:40.545200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:43.540030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:46.173595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:48.943057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:19.842220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:22.222136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:25.634600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:28.350983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:30.913956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:33.343880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:35.827266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:38.382796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:40.767195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:43.744030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:46.387598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:49.164457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:20.055216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:22.404139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:25.884596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:28.580979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:31.136957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:33.554878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:36.055781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:38.579794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:40.987193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:43.996288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-11T11:51:46.620591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-04-11T11:51:56.697501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
actor_1_fb_likesaspect_ratiobudgetcountry_UKcountry_USAcritic_reviews_ratiodirector_fb_likesdurationfacenumber_in_postergrossimdb_classificationmovie_fb_likesnum_voted_usersother_actors_fb_likestitle_year
actor_1_fb_likes1.0000.1450.3890.0000.015-0.1040.1430.2120.1160.3170.0290.1120.4320.7370.119
aspect_ratio0.1451.0000.2660.0000.0000.0820.0570.2160.0350.0940.0000.0760.1230.1150.290
budget0.3890.2661.0000.0000.050-0.1380.1730.3360.0260.5790.0000.0970.5010.3840.143
country_UK0.0000.0000.0001.0000.5560.025-0.0120.0540.003-0.0940.1080.0000.007-0.067-0.017
country_USA0.0150.0000.0500.5561.000-0.1250.050-0.0480.0330.2690.1060.0240.1160.266-0.051
critic_reviews_ratio-0.1040.082-0.1380.025-0.1251.000-0.064-0.2340.055-0.2930.0040.106-0.325-0.1250.580
director_fb_likes0.1430.0570.173-0.0120.050-0.0641.0000.1990.0080.1610.1390.0430.2560.117-0.019
duration0.2120.2160.3360.054-0.048-0.2340.1991.0000.0490.2440.2090.1070.3580.169-0.075
facenumber_in_poster0.1160.0350.0260.0030.0330.0550.0080.0491.000-0.0220.007-0.012-0.0410.1130.064
gross0.3170.0940.579-0.0940.269-0.2930.1610.244-0.0221.0000.1220.1040.6270.3550.022
imdb_classification0.0290.0000.0000.1080.1060.0040.1390.2090.0070.1221.0000.1080.373-0.008-0.127
movie_fb_likes0.1120.0760.0970.0000.0240.1060.0430.107-0.0120.1040.1081.0000.2150.0980.273
num_voted_users0.4320.1230.5010.0070.116-0.3250.2560.358-0.0410.6270.3730.2151.0000.3780.029
other_actors_fb_likes0.7370.1150.384-0.0670.266-0.1250.1170.1690.1130.355-0.0080.0980.3781.0000.105
title_year0.1190.2900.143-0.017-0.0510.580-0.019-0.0750.0640.022-0.1270.2730.0290.1051.000

Missing values

2024-04-11T11:51:49.456500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T11:51:49.913015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationdirector_fb_likesactor_1_fb_likesgrossnum_voted_usersfacenumber_in_posterbudgettitle_yearaspect_ratiomovie_fb_likescountry_UKcountry_USAother_actors_fb_likescritic_reviews_ratioimdb_classification
0178.00.01000.0760505847.08862040.0237000000.02009.01.7833000011791.00.2367392
1169.0563.040000.0309404152.04712200.0300000000.02007.02.350016000.00.2439422
2148.00.011000.0200074175.02758681.0245000000.02015.02.358500010554.00.6056342
3164.022000.027000.0448130642.011443370.0250000000.02012.02.351640000146000.00.3010003
5132.0475.0640.073058679.02122041.0263700000.02012.02.3524000011162.00.6260162
6156.00.024000.0336530303.03830560.0258000000.02007.02.3500115000.00.2060992
7100.015.0799.0200807262.02948101.0260000000.02010.01.852900001837.00.8372092
8141.00.026000.0458991599.04626694.0250000000.02015.02.351180000140000.00.5684872
9153.0282.025000.0301956980.03217953.0250000000.02009.02.35100001021000.00.3854062
10183.00.015000.0330249062.03716390.0250000000.02016.02.35197000016000.00.2229952
durationdirector_fb_likesactor_1_fb_likesgrossnum_voted_usersfacenumber_in_posterbudgettitle_yearaspect_ratiomovie_fb_likescountry_UKcountry_USAother_actors_fb_likescritic_reviews_ratioimdb_classification
5026110.0107.0576.0136007.039241.04500.02004.02.3517100178.02.0769232
502790.0397.05.0673780.045550.010000.02000.01.85697000.02.4615382
5029111.062.089.094596.063180.01000000.01997.01.858170019.01.5600002
503298.03.0789.024848292.04381.020000000.01995.02.352001346.00.7142862
503377.0291.0291.0424760.0726390.07000.02004.01.85190000153.00.3854452
503480.00.00.070071.05890.07000.02005.02.3574000.01.0000002
503581.00.0121.02040920.0520550.07000.01992.01.3700126.00.4307692
503795.00.0296.04584.013381.09000.02011.02.3541301338.01.0000002
503887.02.0637.024848292.06292.020000000.02013.02.358400788.00.1666672
504290.016.086.085222.042850.01100.02004.01.854560139.00.5119052

Duplicate rows

Most frequently occurring

durationdirector_fb_likesactor_1_fb_likesgrossnum_voted_usersfacenumber_in_posterbudgettitle_yearaspect_ratiomovie_fb_likescountry_UKcountry_USAother_actors_fb_likescritic_reviews_ratioimdb_classification# duplicates
0101.03.0448.012189514.0300920.023000000.02004.02.35001263.00.50387622